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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.29

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2025-06-06, 10:55 CST based on data in: /home/tripp/GENOMICA/2025_snakemake_tests/results/summary_qc

        General Statistics

        Showing 12/12 rows and 12/16 columns.
        Sample NameError rateNon-primaryReads mapped% Mapped% Proper pairs% MapQ 0 readsTotal seqsMean insert% Aligned% Duplication% > Q30Mb Q30 basesReads After FilteringGC content% PF% Adapter
        batch1
        0.16%
        0.0M
        0.1M
        99.9%
        99.2%
        0.0%
        0.1M
        167.6bp
        99.9%
        batch1_chrI
        0.0%
        92.8%
        7.6Mb
        0.1M
        43.4%
        99.4%
        1.5%
        batch2
        0.15%
        0.0M
        0.1M
        99.9%
        99.4%
        0.0%
        0.1M
        172.8bp
        99.9%
        batch2_chrI
        0.0%
        92.9%
        10.6Mb
        0.1M
        43.4%
        100.0%
        0.6%
        batch3
        0.16%
        0.0M
        0.1M
        99.9%
        99.3%
        0.0%
        0.1M
        168.8bp
        99.8%
        batch3_chrI
        0.0%
        92.8%
        8.1Mb
        0.1M
        43.6%
        99.5%
        1.4%
        chem1
        0.26%
        0.0M
        0.1M
        99.9%
        98.9%
        0.0%
        0.1M
        166.1bp
        99.9%
        chem1_chrI
        0.0%
        92.9%
        6.7Mb
        0.1M
        43.4%
        100.0%
        0.7%
        chem2
        0.25%
        0.0M
        0.1M
        99.9%
        99.0%
        0.0%
        0.1M
        172.7bp
        99.9%
        chem2_chrI
        0.0%
        93.1%
        8.4Mb
        0.1M
        43.3%
        100.0%
        0.5%
        chem3
        0.25%
        0.0M
        0.1M
        99.9%
        99.1%
        0.0%
        0.1M
        172.3bp
        99.9%
        chem3_chrI
        0.0%
        93.1%
        10.1Mb
        0.1M
        43.4%
        100.0%
        0.6%

        Pca Plot

        PCA de expresión génica

        GffCompare

        Version: 0.12.9

        Tool to compare, merge and annotate one or more GFF files with a reference annotation in GFF format.URL: https://ccb.jhu.edu/software/stringtie/gffcompare.shtmlDOI: 10.12688/f1000research.23297.1

        Accuracy values

        Displayed are the accuracy values precisiond and sensitivity for different levels of genomic features. The metrics are calculated for the comparison of a query and reference GTF file.

        Accuracy metrics are calculated as described in Burset et al. (1996). Sensitivity is the true positive rate, Precision True Positives are query features that agree with features in the reference. The exact definition depends on the feature level:

        • Base: True positives are bases reported at the same coordinates.
        • Exon: Comparison units are exons that overlap in query and reference with same coordinates.
        • Intron chain: True positives are query transcripts for which all introns coordinates match those in the reference.
        • Transcript: More stringent then intron chain, all Exon coordinates need to match. Outer exon coordinates (start + end) can vary by 100 bases in default settings
        • Locus: Cluster of exons need to match.

        A more in depth description can be found here.

        Created with MultiQC

        Novel features

        Number of novel features, present in the query data but not found in the reference annotation.

        Created with MultiQC

        Missing features

        False negative features, which are found in the reference annotation but missed (not present) in the query data.

        Created with MultiQC

        Samtools

        Version: 1.20 HTSlib: 1.21

        Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Created with MultiQC

        Bowtie 2 / HiSAT2

        Results from both Bowtie 2 and HISAT2, tools for aligning reads against a reference genome.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.
        Created with MultiQC

        fastp

        Version: 0.23.4

        All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

        Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with MultiQC

        Insert Sizes

        Insert size estimation of sampled reads.

        Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        GffCompareGffCompare0.12.9
        SamtoolsHTSlib1.21
        Samtools1.20
        fastpfastp0.23.4

        MultiQC v1.29 - Written by Phil Ewels, available on GitHub.

        This report uses Plotly, jQuery, jQuery UI, Bootstrap and FileSaver.js.